Seasonal upwelling influenced by EACC and the monsoon winds. Key characteristics include:
Seasonality
Nutrient enrichment
Ecological impact
Economic importance
Environmental Variables
Fish populations are heavily influenced by their environment. Understanding these links is crucial for modern stock assessment.
5. Environmental Data
Environmental factors affect fish:
Distribution & Migration
Growth rates
Reproduction (spawning timing & success)
Mortality (survival rates)
Key Environmental Variables
These variables help explain changes in fish populations that catch and effort data alone cannot.
Commonly Used Variables:
Sea Surface Temperature (SST): Influences metabolic rates, growth, and species distribution.
Chlorophyll-a (Chl-a): A proxy for phytoplankton and primary productivity (i.e., food availability).
Salinity: Affects species tolerance and distribution, especially in coastal and estuarine areas.
Oceanographic Features:
Ocean Currents: Drive larval dispersal and create migration pathways (e.g., the Somali Current).
Upwelling: Brings nutrient-rich deep water to the surface, boosting productivity.
Climate Indices: Large-scale patterns like the Indian Ocean Dipole (IOD) or El Niño (ENSO) that impact regional conditions.
Seasonal Surface Ocean Circulation
Seasonal Wind Circulation
Seasonal Sea Surface Temperature
Data Integration
Sources & Integration
Data Sources: Primarily from satellite remote sensing (e.g., NASA, NOAA, Copernicus), oceanographic models, and research buoys.
Use in Assessments:
Explain variability in abundance indices (e.g., why CPUE is high in some years).
Improve recruitment predictions by linking larval survival to environmental conditions.
Define stock boundaries based on habitat suitability.
This leads to environmentally-explicit or ecosystem-based stock assessment.
Data Quality Considerations
The “GIGO” principle: Garbage In, Garbage Out.
Accuracy & Precision: Is the data correct and consistent?
Bias: Does the data systematically over/underestimate the truth?
Completeness: Are there gaps in the time series?
Standardization: Are data collected the same way over time?
Data Limitations in the Indian Ocean
Common challenges faced across the region that impact stock assessment.
Small-Scale Fisheries
🛶
Dispersed, artisanal fleets are difficult to monitor comprehensively.
Multi-Species Catches
🐟🐠
It is hard to separate catch data for individual species in mixed fisheries.
Limited Resources
💰🚫
Low financial and human capacity for consistent, long-term data collection.
Transboundary Stocks
🗺️🐟
Fish cross national borders, but data collection programs often do not.
Challenges in Somali Fisheries
Long & Dispersed Coastline
🗺️
Many remote landing sites make comprehensive data collection difficult.
Historical Data Gaps
🗓️➡️❓
Inconsistent records from past decades complicate trend analysis.
Logistical & Security Hurdles
🚧
Accessing some areas for monitoring can be challenging and unsafe.
Opportunities in Somali Fisheries
Visualizing the key opportunities for enhancing data collection.
Community-Based Data
👥
Engaging local fishers and communities to gather valuable, on-the-ground data.
Modern Technology
📱
Using mobile apps and vessel tracking to improve data accuracy and coverage.
Regional Collaboration
🤝
Partnering with neighboring countries and organizations to share knowledge.
Working with Incomplete Data
We don’t need perfect data to start! Data-limited methods are designed for these situations.
Strategies for Data-Limited Situations
When data is scarce, we adapt our methods to focus on what we can learn.
Use Proxies
📏➡️🗓️
Use easily collected data (like length) to infer harder-to-get data (like age).
Borrow Information
📚
Apply known biological parameters from similar, well-studied species or regions.
Focus on Trends
📈
Analyze the direction of change (e.g., in CPUE or average size) over time.
Employ Simpler Models
🧰
Use robust methods like Catch-MSY or length-based indicators that require less data.
Summary
Good data is the cornerstone of reliable stock assessment.
We need catch, effort, biological, and abundance index data.
Data quality is as important as data quantity.
Even with limitations, robust science is possible using data-limited approaches.
TOOLS FOR INNOVATIONS
Coding with Programming languages (Python & R)
Exercise
Somali Landing Site Data
Using the provided lw dataset from Somali landing sites:
Review the Data: Explore the length, weight, and catch data available for your assigned region. What are the main species? What are the temporal and spatial trends?
Identify Data Gaps: What information is missing? Are there gaps in time? Are certain species or gear types underrepresented?
Assess Data Quality: Look for potential issues like outliers, missing values, or inconsistent entries. How might these affect an assessment?
Propose Improvements: Based on your review, suggest 2-3 practical steps to improve data collection for stock assessment in your region.